Interactive Multi-level Stroke Control for Neural Style Transfer

We present StyleTune, a mobile app for interactive multi-level control of neural style transfers that facilitates creative adjustments of style elements and enables high output fidelity. In contrast to current mobile neural style transfer apps, StyleTune supports users to adjust both the size and orientation of style elements, such as brushstrokes and texture patches, on a global as well as local level. To this end, we propose a novel stroke-adaptive feed-forward style transfer network, that enables control over stroke size and intensity and allows a larger range of edits than current approaches. For additional level-of-control, we propose a network-agnostic method for stroke-orientation adjustment by utilizing the rotation-variance of Convolutional Neural Networks (CNNs). To achieve high output fidelity, we further add a patch-based style transfer method that enables users to obtain output resolutions of more than 20 Megapixel (Mpix). Our approach empowers users to create many novel results that are not possible with current mobile neural style transfer apps.